System and method for fault detection of components using information fusion technique

ABSTRACT

An example method comprises receiving historical sensor data of a first time period, the historical data including sensor data of a renewable energy asset, extracting features, performing a unsupervised anomaly detection technique on the historical sensor data to generate first labels associated with the historical sensor data, performing at least one dimensionality reduction technique to generate second labels, combining the first labels and the second labels to generate combined labels, generating one or more models based on supervised machine learning and the combined labels, receiving current sensor data of a second time period, the current sensor data including sensor data of the renewable energy asset, extracting features, applying the one or more models to the extracted features of the current sensor data to create a prediction of a future fault in the renewable energy asset, and generating a report including the prediction of the future fault in the energy asset.

FIELD OF THE INVENTION

Embodiments of the present invention(s) relate generally to faultdetection in electrical networks. In particular, the presentinvention(s) relate to fault detection in electrical networks throughcombining labels from a reduced dimension analytic approach with anon-reduced dimension analytic approach to generate models for improvedfault detection and action.

DESCRIPTION OF RELATED ART

Detection and prediction of failure in one or more components of anasset of an electrical network has been difficult. Detection of afailure of a component of an asset is tedious and high in errors. Inthis example, an asset is a device for generating or distributing powerin an electrical network. Examples of assets can include, but is notlimited to, a wind turbine, solar panel power generator, converter,transformer, distributor, and/or the like. Given that detection of afailure of a component of an asset may be difficult to determine,increased accuracy of prediction of future failures compounds problems.

SUMMARY

An example non-transitory computer readable medium comprising executableinstructions, the executable instructions being executable by one ormore processors to perform a method, the method comprising receivinghistorical sensor data of a first time period, the historical dataincluding sensor data from one or more sensors of a renewable energyasset, extracting features from the historical sensor data, performing aunsupervised anomaly detection technique on the historical sensor datato generate first labels associated with the historical sensor data,performing at least one dimensionality reduction technique to generatesecond labels associated with the historical sensor data, thedimensionality reduction technique reducing a second number ofdimensions of the extracted features when compared to a first number ofdimensions of the extracted features analyzed using the unsupervisedanomaly detection, combining the first labels and the second labels togenerate combined labels, generating one or more models based onsupervised machine learning and the combined labels, receiving currentsensor data of a second time period, the current sensor data includingsensor data from at least a subset of the one or more sensors of therenewable energy asset, extracting features from the current sensordata, applying the one or more models to the extracted features of thecurrent sensor data to create a prediction of a future fault in therenewable energy asset, and generating a report including the predictionof the future fault in the renewable energy asset.

In various embodiments, the unsupervised anomaly detection techniqueincludes an isolation forest technique. The at least one dimensionalityreduction technique may include a principal component analysis (PCA)technique. The combination of the first and second labels may becombined in a complimentary manner. In one example, wherein generatingone or more models based on supervised machine learning and the combinedlabels includes K-nearest neighbor algorithm or a neural network. Insome embodiments, the supervised machine learning includes k-meansclustering.

Performing the unsupervised anomaly detection technique on thehistorical sensor data to generate the first labels may comprisegenerating an anomaly score based on an output of the supervised anomalydetection techniques and comparing the anomaly score to a threshold todetermine if at least one of the first labels should be generated. Invarious embodiments, performing the at least one dimensionalityreduction technique to generate the second labels comprises generating az-score based on an output of the at least one dimensionality reductiontechnique and comparing the z-score to a threshold to determine if atleast one of the second labels should be generated.

The method may further comprise comparing a fault prediction against acriteria to determine significance of one or more predicted faults andgenerating an alert based on the comparison, the alert includinggenerating a message identifying the one or more predicted faults.

An example system comprises at least one processor and memory containinginstructions, the instructions being executable by the at least oneprocessor to: receive historical sensor data of a first time period, thehistorical data including sensor data from one or more sensors of arenewable energy asset, extract features from the historical sensordata, perform a unsupervised anomaly detection technique on thehistorical sensor data to generate first labels associated with thehistorical sensor data, perform at least one dimensionality reductiontechnique to generate second labels associated with the historicalsensor data, the dimensionality reduction technique reducing a secondnumber of dimensions of the extracted features when compared to a firstnumber of dimensions of the extracted features analyzed using theunsupervised anomaly detection, combine the first labels and the secondlabels to generate combined labels, generate one or more models based onsupervised machine learning and the combined labels, receive currentsensor data of a second time period, the current sensor data includingsensor data from at least a subset of the one or more sensors of therenewable energy asset, extract features from the current sensor data,apply the one or more models to the extracted features of the currentsensor data to create a prediction of a future fault in the renewableenergy asset, and generate a report including the prediction of thefuture fault in the renewable energy asset.

An example method comprises receiving historical sensor data of a firsttime period, the historical data including sensor data from one or moresensors of a renewable energy asset, extracting features from thehistorical sensor data, performing a unsupervised anomaly detectiontechnique on the historical sensor data to generate first labelsassociated with the historical sensor data, performing at least onedimensionality reduction technique to generate second labels associatedwith the historical sensor data, the dimensionality reduction techniquereducing a second number of dimensions of the extracted features whencompared to a first number of dimensions of the extracted featuresanalyzed using the unsupervised anomaly detection, combining the firstlabels and the second labels to generate combined labels, generating oneor more models based on supervised machine learning and the combinedlabels, receiving current sensor data of a second time period, thecurrent sensor data including sensor data from at least a subset of theone or more sensors of the renewable energy asset, extracting featuresfrom the current sensor data, applying the one or more models to theextracted features of the current sensor data to create a prediction ofa future fault in the renewable energy asset, and generating a reportincluding the prediction of the future fault in the renewable energyasset

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a block diagram of an example of an electrical network insome embodiments.

FIG. 2A depicts a training model and scoring model utilized in the priorart.

FIG. 2B depicts a training model and scoring model utilized in anotherexample in the prior art

FIG. 3 depicts a block diagram for fault detection and prediction insome embodiments.

FIG. 4 depicts a component failure prediction system in someembodiments.

FIG. 5 depicts an example process where historical sensor data of ahigher dimensionality is analyzed, the same historical sensor data of alower (reduced) dimensionality is separately analyzed, and labelsgenerated by the two separate analytical processes are combined.

FIG. 6 depicts information fusion in some embodiments.

FIG. 7a depicts the results of running an isolation forest analysis onsensor data with a threshold of 50% as an example.

FIG. 7b depicts the results of running a PCA analysis on sensor datawith a threshold of 50% as an example.

FIG. 7c depicts example results of fusing the results of the isolationforest analysis and the PCA analysis

FIG. 8 depicts a flow chart for creating models using label fusion insome embodiments.

FIG. 9 depicts a flow chart for applying models using label fusion insome embodiments.

FIG. 10 depicts an example report for fault and prediction analysis foran electrical asset using information fusion.

FIG. 11 depicts an example report for fault and prediction analysis foranother electrical asset using information fusion.

FIG. 12 depicts an example report for fault and prediction analysis fora third electrical asset using information fusion.

FIG. 13 depicts a block diagram of an example computer system accordingto some embodiments.

DETAILED DESCRIPTION

In wind and solar generation industry, it is important to forecastcomponent failures with additional lead time. Some embodiments describedherein utilize machine learning algorithms to build a sophisticatedforecasting model based on multi-variate sensor data to forecastcomponent failures. Various embodiments described herein overcomelimitations of the prior art by providing scalability, proactivewarnings, and computational efficiency while improving accuracy.

In various embodiments described herein, labels based on past data of anelectrical network may be generated in at least two different ways. Forexample, one way is through a reduced-dimensionality approach andanother way is through a non-dimensionality reduced approached (or twoways in which dimensions are reduced more in a first method than asecond method). The labels may be combined (e.g. “fused” in acomplementary method), and then the combined label set may be used toassist in training a model for predicting failures. A dimension in thisexample may be a feature (e.g., a column or variable) of data.

FIG. 1 depicts a block diagram 100 of an example of an electricalnetwork 102 in some embodiments. FIG. 1 includes an electrical network102, a component failure prediction system 104, a power system 106, incommunication over a communication network 108. The electrical network102 includes any number of transmission lines 110, renewable energysources 112, substations 114, and transformers 116. The electricalnetwork 102 may include any number of electrical assets includingprotective assets (e.g., relays or other circuits to protect one or moreassets), transmission assets (e.g., lines, or devices for delivering orreceiving power), and/or loads (e.g., residential houses, commercialbusinesses, and/or the like).

Components of the electrical network 102 such as the transmissionline(s) 110, the renewable energy source(s) 112, substation(s) 114,and/or transformer(s) 106 may inject energy or power (or assist in theinjection of energy or power) into the electrical network 102. Eachcomponent of the electrical network 102 may be represented by any numberof nodes in a network representation of the electrical network.Renewable energy sources 112 may include solar panels, wind turbines,and/or other forms of so called “green energy.” The electrical network102 may include a wide electrical network grid (e.g., with 40,000 assetsor more).

Each component of the electrical network 102 may represent one or moreelements of their respective components. For example, the transformer(s)116, as shown in FIG. 1 may represent any number of transformers whichmake up electrical network 102.

In some embodiments, the component failure prediction system 104provides failure prediction based on models created from past data fromregarding one or more components of the electrical network 102 (asdescribed herein).

In some embodiments, communication network 108 represents one or morecomputer networks (e.g., LAN, WAN, and/or the like). Communicationnetwork 108 may provide communication between any of the componentfailure prediction system 104, the power system 106, and/or theelectrical network 102. In some implementations, communication network108 comprises computer devices, routers, cables, uses, and/or othernetwork topologies. In some embodiments, communication network 108 maybe wired and/or wireless. In various embodiments, communication network108 may comprise the Internet, one or more networks that may be public,private, IP-based, non-IP based, and so forth.

The component failure prediction system 104 may include any number ofdigital devices configured to forecast component failure of any numberof components and/or generators (e.g., wind turbine or solar powergenerator) of the renewable energy sources 112.

In various embodiments, the component failure prediction system 104 mayreduce computational burden of forecasting failure of any number ofcomponents and/or generators by applying machine learning tools onhistorical data using information fusion as discussed herein.

The power system 106 may include any number of digital devicesconfigured to control distribution and/or transmission of energy. Thepower system 106 may, in one example, be controlled by a power company,utility, and/or the like. A digital device is any device with at leastone processor and memory. Examples of systems, environments, and/orconfigurations that may be suitable for use with system include, but arenot limited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

A computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types. Adigital device, such as a computer system, is further described withregard to FIG. 13.

FIGS. 2A and 2B depict different processes of fault detection and faultprediction in the prior art. Both prior art processes depend upon atedious, computationally efficient, and erroneous process of labelinghistorical data which is then fed into a supervised learning model togenerate models that can be used on new data for detection andprediction of faults. Because of the way in which labeling is completedin the prior art, any model generated by the training model will havelimitations including inaccuracies and limitations of variability thatimpact the application of the model to new data. As a result, labelingof new data by the scoring model is unreliable, open to increasederrors, and limited in the variability of data it can accept to makepredictions.

FIG. 2A depicts a training model 202 and scoring model 204 utilized inthe prior art. In the example depicted in FIG. 2A, historical sensordata may be received from any number of sensors associated with anynumber of components of an asset (e.g., wind turbines and solar panelgenerators). The historical sensor data may be multivariate sensor data.Multivariate sensor data is generated by a plurality of sensors receivedfrom one or more assets. There may be any number of sensors associatedwith any number of components. Once models are generated using thehistorical sensor data, current (new) multivariate sensor data generatedby the sensors is received by the scoring model 204 which then utilizesthe models from the training model 202 to label and identify existing orfuture failures.

In the prior art, however, labels utilized in modeling by the trainingmodel 202 are often unavailable and unreliable. Labeling sensor datatends to be manual, and, as a result, is slow and expensive. Even ifsome of the labels are available, the labels tend to be incomplete,uncertain, and/or erroneous. One of the reasons for the unreliablelabels is that obtaining reliable historical data is often late due tothe manual process of identifying faults and understanding the data.Labeling faults is tedious given the amount of data over a large numberof sensors. As a result, errors in the label data makes it difficult todevelop more reliable models due to high variance.

Typically, the training model 202 receives the label information (oftenunreliable and incomplete) from any number of sources. The sources mayinclude individuals who have manually created the label data from thehistorical sensor data or other historical sensor data. The trainingmodel 202 may receive historical data from any number of sensors of anynumber of electrical assets. The historical data may be multivariate,time series data.

The training model 202 may perform feature extraction and then generatesupervised model(s) based on the labels and the features extracted fromthe historical data. Subsequently, the scoring model 204 may receivecurrent multivariate sensor data from any number of sources, extractfeatures from the data and apply a supervised model to the extractedfeatures to identify applicable labels based on them model(s) from thetraining model 202.

Once the models are created using the unreliable labels and historicalsensor data, the scoring model 204 may receive new (e.g., “current”)sensor data from the same or similar sensors of assets. The scoringmodel 204 may extract features from the current sensor data in a mannersimilar to that of the training model 202. The scoring model 204 appliesto the supervised model generated by the training model 202 to label astate (e.g., condition) of an asset, scenario, or asset as potentiallyin failure or may be in failure.

As discussed herein, in order for the scoring model 204 to identifyfailures or forecast failures, the scoring model 204 must rely on themodels generated by the training model 202. However, the modelsgenerated by the training model 202 depend upon the unreliable labeldata and, as such, produces errors, affects failure detection, and leadsto erroneous predictions.

FIG. 2B depicts a training model 202 and scoring model 204 utilized inanother example in the prior art. In order to avoid creating manuallabels that may be incomplete and/or erroneous, the training model 202may utilize an unsupervised learning model to generate the label datafrom extracted features of the historical sensor data.

In this example, once the training model 202 extracts features in amanner similar to that discussed in FIG. 2A, the training model mayapply an unsupervised learning model to generate the label data.Unsupervised learning learns from the extracted features which had notbeen previously labeled, classified, or categorized. Unsupervisedlearning identifies commonalities in the data and may react to thepresence or absence of such commonalities to generate labels.Unsupervised learning, however, tends to look at similarities (asdefined by some metric) in the data to separate the data into groups.The definition and measure of similarities tends to group dataunpredictably and in a manner that may not be explainable or accurate.

Once the models are created using the unsupervised learning model labelsand historical sensor data, the scoring model 204 receives new (e.g.,“current”) sensor data from the same or similar sensors of assets. Thescoring model 204 extracts features from the current sensor data andthen applies the model generated by the training model 202 to label astate (e.g., condition) of an asset, scenario, or asset as potentiallyin failure or may be in failure.

In order for the scoring model 204 to identify failures or forecastfailures, the scoring model 204 must rely on the models generated by thetraining model 202. However, because of the nature of unsupervisedlearning, the labels generated by the scoring model 204 have difficultycapturing low variability with high and low dimensionality data.Accuracy can suffer, and the process is tedious as well ascomputationally inefficient. Further, thresholds need to be defined tomake conclusions based on the labels, however, this further lends toinaccurate fault detection and fault prediction (e.g., false positivesor incorrect negatives).

FIG. 3 depicts a block diagram for fault detection and prediction insome embodiments. The fault detection and prediction system discussedherein may assist in the detection of component failure and forecast ofcomponent failure of assets and electrical network. These assets caninclude, for example, wind turbines and solar panel generators. Examplesof component failures in wind turbines include failures in a mainbearing, gearbox, generator, or anemometer. Failures in solar panelgenerators often occur as a result of failures in an inverter, paneldegradation, and an IGBT.

A wind turbine has many potential components of failure. Differentsensors may provide different readings for one or more differentcomponents or combinations of components. Given the number of windturbines in a wind farm, the amount of data to be assessed may beuntenable using prior art methods. For example, data analytics systemsof the prior art do not scale, sensors provide too much data to beassessed by the prior art systems, and there is a lack of computationalcapacity in prior art systems to effectively assess data from wind farmsin a time sensitive manner. As a result, prior art systems are reactiveto existing failures rather than proactively providing reports orwarnings of potential future failure of one or more components.

For example, various embodiments regarding a wind turbine describedherein may identify potential failure of a main bearing, gearbox,generator, or anemometer of one or more wind turbines. Although manybearings may be utilized in a wind turbine (e.g., yaw and pitchbearings), the main shaft and gearbox of the wind turbine tend to be themost problematic. For example, a main bearing may fail due to highthrust load or may fail due to inadequate lubricant film generation.Trends in redesign of a main shaft and/or gearbox of a single windturbine have been driven by unexpected failures in these units. Theunplanned replacement of main-shaft bearing can cost operators up to$450,000 and have an obvious impact on financial performance.

Gearbox failures are one of the largest sources of unplanned maintenancecosts. Gearbox failures can be caused by design issues, manufacturingdefects, deficiencies in the lubricant, excessive time at standstill,high loading, and other reasons. There may be many different modes ofgearbox failure and, as such, it may be important to identify the typeof failure mode in addressing the failure. One mode is micropittingwhich occurs when lubricant film between contacting surfaces in agearbox is not thick enough. Macropitting occurs when contact stress ina gear or breaking exceeds the fatigue strength of the material. Bendingfatigue a failure mode that affects gear teeth and axial cracking mayoccur in bearings of a gearbox; the cracks develop in the axialdirection, perpendicular to the direction of rolling.

The generator typically converts the wind energy to electrical energy.Failures often occur in bearings, stator, rotor, or the like which canlead to inconsistent voltage to total failure. Generator failure may bedifficult to detect as a result of inconsistent weather, lack of motion,and/or partial failure of the anemometer.

The anemometer uses moving parts as sensors. Anemometers often include“cups” for wind speed measurements and a wind vane that uses a “vanetail” for measuring vector change, or wind direction. Freezing weatherhas caused the “cups” and “vane tail” to lock. If an anemometerunder-reports wind speed because of a partial failure, there is anincrease in rotor acceleration that indicates a large amount of windenergy is not converted into electrical engineering. Rolling resistancein an anemometer bearings typically increase over time until they seize.Further, if the anemometer is not accurate, the wind turbine will notcontrol blade pitch and rotor speed as needed. Poor or inaccuratemeasurements by the anemometer will lead to incorrect adjustments andincreased fatigue.

Similarly, various embodiments regarding a solar panel generatordescribed herein may identify potential failure of a inverter, solarpanel, and IGBT in one or more solar panels of a solar farm.

A solar inverter is an electrical converter to convert variable directcurrent from a photovoltaic solar panel into a utility frequencyalternating current that can be fed to an electrical grid. Productionloses are often attributable to poor performance of inverters. Solarinventers may overheat (caused by weather, use, or failure of coolingsystems) which can reduce production. Moisture may cause a short circuitwhich can cause complete or partial failure (e.g., to a minimum“required” isolation level). Further, failure of the solar inverter torestart after gird fault may require manual restarting of the equipment.

The panel refers to the solar or photovoltaic panel. The photovoltaicpanel may degrade due to weather, poor cleaning, thermal cycling, dampheat, humidity freezing, and UV exposure. Thermal cycling can causesolder bond failures and cracks. Damp heat has been associated withdelamination of encapsulants and corrosion of cells. Humidity freezingcan cause junction box adhesion to fail. UV exposure contributes todiscoloration and backsheet degradation.

Solar inverters often use insulated gate bipolar transistors (IGBT) forconversion of solar panel output to AC voltage. Failures in the IGBT canbe caused by fatigue, corrosion of metallizations, electromigration ofmetalizations, conductive filament formation, stress driven diffusionvoiding, and time dependent dielectric breakdown.

Returning to FIG. 3, data sources 302 may be or include any sources ofdata including multivariate sensor data. In some embodiments, the datasources 302 may include the sensors themselves. The data sources 302 maybe hosted or operated by the asset/electrical network operator, owner,public utility, and/or the like. For example, the sensors of a pluralityof electrical assets may generate time series data which may becollected in the data sources 302 by a public utility, operator,administrator, and/or the like.

The component failure prediction system 104 may receive the multivariatesensor data from one or more of the data sources 302. On level 1, thecomponent failure prediction system 104 receives and/or retrieves theraw data from the multivariate sensor data. On level 2, the componentfailure prediction system 104 performs feature extraction from the data(e.g., the raw data or directly from the multivariate sensor data). Onlevel 3, the component failure prediction system 104 determines labelsand generates one or more machine learning models using the labels.

Machine learning models may be generated utilizing labels usinginformation fusion. Fault detection by information fusion utilizes atleast two different methodologies to generate label information fromhistorical sensor data (past data from any number of sensors of anynumber of electrical devices). For example, the component failureprediction system 104 may first utilize a first process that does notreduce dimensionality of the historical data to generate and/or identifylabels. The fault detection by information fusion may also utilize asecond process that reduces dimensionality of the historical data andthen generates and/or identifies labels. The results of the twoprocesses may be combined (e.g., “fused”) to generate combinedhistorical labels that may be utilized to generate one or more models.The models may be utilized by the component failure prediction system104 to identify new states (e.g., using labels) in new sensor data(e.g., “current sensor data”). In level 4, the component failureprediction system 104 may make decisions using the new states and/orlabels (e.g., through thresholding, grouping, classification, and/or thelike) to detect and/or predict faults.

FIG. 4 depicts a component failure prediction system 104 in someembodiments. The component failure prediction system 104 comprises acommunication module 402, a primary analytics module 404, a reduceddimensionality analytics module 406, fusion module 408, model trainingmodule 410, feature extraction module 412, model application module 414,evaluation module 416, report and alert generation module 418, and datastorage 420.

In some embodiments, the component failure prediction system 104receives historical data from any number of sensors of any number ofelectrical assets, analyzes a reduced dimensionality of the data toidentify labels, analyzes a higher dimensionality of the data to alsoproduce labels, and combine the two sets of labels to assist in modeltraining. Models that are produced from this process may then beutilized to detect and/or predict failures of one or more components ofthe one or more electrical assets.

The communication module 402 may be configured to transmit and receivedata between two or more modules in component failure prediction system104. In some embodiments, the communication module 402 is configured toreceive information (e.g., historical sensor data and/or current sensordata) regarding assets of the electrical network 102 (e.g., from thepower system 106, sensors within components of the electrical network102 such as the renewable energy sources 112, third-party systems suchas government entities, other utilities, and/or the like).

The communication module 402 may be configured to receive historicaldata regarding electrical assets either individually or in combination(e.g., wind turbines, solar panels, windfarms, solar farms, componentsof devices, components of wind turbines, components of solar panels,substations 114, transformers 116, and/or transmission lines 110). Thecommunication module 402 may further receive sensor data from one ormore sensors of any number of electrical assets such as those describedabove.

The feature extraction module 412 may extract features (e.g., dimensionsand/or variables) from the received historical sensor data. Themultivariate sensor data may, as discussed herein, be time series data.For example, the feature extraction module 412 may extract features fromthe time series data. The feature extraction module 412 may provide theextracted features to the primary analytics module 404 and the reduceddimensionality analytics module 406.

In various embodiments, feature extraction may also refer to the processof creating new features from an initial set of data. These features mayencapsulate central properties of a data set and represent the data setand a low dimensional space that facilitates learning. As can beappreciated, the initial multivariate sensor data may include a numberof features that are too large and unwieldy to be effectively managedand may require an unreasonable amount of computing resources. Featureextraction may be used to provide a more manageable representativesubset of input variables. It will be appreciated that featureextraction may extract features for the data as well as create newfeatures from the initial set of data.

It will be appreciated that, in some embodiments, dimensions may referto columns (e.g., features or variables) of the received historicalsensor data.

The primary analytics module 404 may receive historical sensor data fromthe communication module 402 or extracted features of the receivedhistorical sensor data from the feature extraction module 412. Thenumber of dimensions of the historical sensor data and/or number ofdimensions of the extracted features analyzed by the primary analyticsmodule 404 may be greater than the number of dimensions of thehistorical sensor data and/or number of dimensions of the extractedfeatures analyzed by the reduced dimensionality analytics module 406.Examples of processes of analysis by the primary analytics module 404are discussed herein.

The reduced dimensionality analytics module 406 may reducedimensionality of all or some of the historical sensor data and/orextracted features before analyzing the data. In some embodiments,dimensionality reduction may be utilized to map time series to a lowerdimensional space. Time series data may be decomposed into componentsthat represent one or more patterns. The components, or the parametersassociated with the patterns, represent features of a time series thatcan be used in models. For example, time series data may be clusteredinto common patterns. Trend and classical decomposition may utilize aseries of moving averages to decompose time series data to extractfeatures.

It will be appreciated that any form of decomposition and/or featureextraction may be utilized. For example, instead of trend decomposition,singular spectrum analysis that applies an adaptation of principalcomponent analysis (PCA) may be utilized to decompose time series data.Principal components may then be utilized to forecast and model eachseparately and, in some embodiments, aggregate the component seriesforecasts to forecast the original series.

FIG. 5 depicts an example process where historical sensor data of ahigher dimensionality is analyzed, the same historical sensor data of alower (reduced) dimensionality is separately analyzed, and labelsgenerated by the two separate analytical processes are combined. In someembodiments, features may be extracted from the historical sensor databy the feature extraction module 412. The primary analytics module 404may analyze the extracted data of the received historical sensor datawithout reducing the dimensionality of the extracted features. Invarious embodiments, the primary analytics module 404 reducesdimensionality of the extracted features but there is greaterdimensionality than that which is reduced by the reduced dimensionalityanalytics module 406. Similarly, the reduced dimensionality analyticsmodule 406 may receive the extracted features and further reduce thedimensionality of the extracted features.

In the example of step 502 the communication module 402 receivesmonitored sensor data from any number of sources. Monitored sensor datamay be historical sensor data from any number of sources of any numberof electrical assets of an electrical network. In some embodiments, aportion of the historical sensor data may be analyzed and anotherportion of the struggles sensor data may be used to test models (e.g.,against historical data with ground truth to determine the accuracy ofthe models). In step 504, the feature extraction module 412 may extractfeatures from the historical sensor data received by the communicationmodule 402.

The communication module 402 may send any amount of the historicalsensor data (e.g., the extracted features) to the primary analyticsmodule 404 in step 506 and may send the same historical sensor data(e.g., the same extracted features) to the reduced dimensionalityanalytics module 406 in step 508. In this example, the primary analyticsmodule 404 and the reduced dimensionality analytics module 406 receivethe same data. In some embodiments, the primary analytics module 404 andthe reduced dimensionality analytics module 406 receive different data.

In various embodiments, the feature extraction module 412 extractsfeatures from the historical sensor data received from the communicationmodule 402 and then provides the extracted features (e.g., the sameextracted features) to the primary analytics module 404 and the reduceddimensionality analytics module 406. It will be appreciated that theremay be any number of feature extraction modules 412 that mayindividually provide extracted features to the primary analytics module404 and reduced dimensionality analytics module 406.

In some embodiments, the primary analytics module 404 analyzes thereceived data (e.g., extracted features) without reducing thedimensionality of the data. In various embodiments, the primaryanalytics module 404 reduces dimensionality of the data but there isgreater dimensionality after reduction than after reduction performed bythe reduced dimensionality analytics module 406.

In this example, the primary analytics module 404 analyzes the receiveddata without reducing the dimensionality of the data. The primaryanalytics module 404 may perform anomaly detection (e.g., outlierdetection) on the received historical sensor data to identify data,events, or observations that differ from the majority of the receivedhistorical sensor data. Anomalies may be referred to as novelties,noise, deviations, or exceptions.

The primary analytics module 404 may perform, for example, unsupervisedanomaly detection techniques to detect anomalies in unlabeled receivedhistorical sensor data. Anomalies may be detected by identifyinginstances that appear to fit least in the data set. The primaryanalytics module 404 may perform any type of analytics. For example, theprimary analytics module 404 may perform k-nearest neighbor, localoutlier factor, or isolation forests on the received historical sensordata.

In one example, the primary analytics module 404 may analyze thereceived historical data using isolation forests. Isolation in thisexample means “separating an instance from the rest of the instances.”Since there may be few and different anomalies, they are moresusceptible to isolation. In a data-induced random tree, partitioning ofinstances may be repeated recursively until instances are isolated.Random partitioning may produce noticeable shorter paths for anomaliessince (a) the fewer instances of anomalies result in a smaller number ofpartitions—shorter paths in a tree structure, and (b) instances withdistinguishable attribute-values are more likely to be separated inearly partitioning. As a result, when a forest of random treescollectively produce shorter path lengths for some particular points,then they are highly likely to be anomalies.

For example, partitions may be generated by randomly selecting anattribute and then randomly selecting a split value between the maximumand minimum values of the selected attribute. Since recursivepartitioning can be represented by a tree structure, the number ofpartitions required to isolate a point may be equivalent to the pathlength from the root node to a terminating node. Since each partition israndomly generated, individual trees may be generated with differentsets of partitions. Path lengths may be averaged over a number of treesto find the expected path length.

In step 510 in this example, the primary analytics module 404 mayperform anomaly detection (e.g., using isolation forests) to identify apattern of anomaly and normal data (e.g., based, in part, on pathlengths). In step 512, the primary analytics module 404 may determinemodel parameters of different potential models determined using theisolation forests and then may score the data in step 514 to outputlabels in step 516.

For example, utilizing isolation forests, the primary analytics module404 may detect anomalies in the received historical data and rank theanomalies by degree of anomaly (e.g., based on length of path and/or incomparison to “normal” data). The primary analytics module 404 may sortdata points according to their path lengths or anomaly scores. A pathlength of a point may be measured by the number of edges the pointtransverses a tree from the toot node until the traversal is terminatedat an external node.

The primary analytics module 404 may generate an anomaly score. Forexample, the primary analytics module 404 may estimate an average pathlength of a tree. Given a data set of n instances, the average pathlength of unsuccessful search in Binary Search Tree may be:c(n)=2H(n−1)−(2(n−1)/n)

where H(i) is the harmonic number and it can be estimated byln(i)+0.5772156649 (Euler's constant). As c(n) is the average of h(x)given n, it can be used to normalise h(x). The anomaly score s of aninstance x may be defined as:

${s\left( {x,n} \right)} = 2^{\frac{- {E{({h{(x)}})}}}{c{(n)}}}$

where E(h(x)) is the average of h(x) from a collection of isolationtrees.

In various embodiments, the primary analytics module 404 may label andoutput anomalies in step 514. For example, the primary analytics module404 may compare each anomaly score to a threshold to determine if asignificant anomaly has been detected and should be labeled. Thethreshold may be based on the anomaly scores (e.g., based on adistribution or averaging), the likelihood of fault (e.g., an expectedlikelihood based at least in part on probability), the historical sensordata, or in any number of ways.

In parallel, serial, or asynchronously, the reduced dimensionalityanalytics module 406 may analyze the received historical sensor data(e.g., the extracted features of the received historical sensor data) byreducing dimensionality and/or analyzing the historical sensor datausing analysis that reduces the dimensionality of the data. In oneexample, the reduced dimensionality analytics module 406 may performprincipal component analysis (PCA) (e.g., using the covariance method)on the received historical sensor data to determine uncorrelatedvariables (distinct principal components) in the data. Thetransformation of the data may capture much of the variability in thedata. The resulting vectors may be an uncorrelated orthogonal basis set.

For example, the reduced dimensionality analytics module 406 maydetermine an empirical mean along each dimension (e.g., column) of thehistorical sensor data, calculate deviations from the mean, and find thecovariance matrix from the outer product (e.g., of the mean-subtracteddata matrix). The reduced dimensionality analytics module 406 may findthe covariance matrix and find the eigenvectors and eigenvalues of thecovariance matrix. The columns of eigenvector matrix and the eigenvaluematrix may be sorted in decreasing eigenvalue (maintaining the correctpairings between the two matrixes), and a cumulative energy content maybe computed for each eigenvector. The reduced dimensionality analyticsmodule 406 may select a subset of the eigenvectors as basis vectors andproject z-scores of the data onto the new basis.

While PCA is discussed herein, It will be appreciated that this is onlyone example, there may be any number of analytical methods to applyreduced dimensionality analysis to the received historical sensor data.

The reduced dimensionality analytics module 406 may generate a faultdetection model based on the principal components of the data in step518 and save model parameters in step 520. In step 522, the reduceddimensionality analytics module 406 may score the new data to generatelabel output in step 524.

In various embodiments, the reduced dimensionality analytics module 406may label and output the labels in step 524. For example, the reduceddimensionality analytics module 406 may compare each score to athreshold to determine if a significant anomaly has been detected andshould be labeled. The threshold may be based on the z scores, thelikelihood of fault (e.g., an expected likelihood based at least in parton probability), the historical sensor data, or in any number of ways.

In step 526, the fusion module 408 may fuse the two sets of labels. Forexample, the fusion module 408 may combine (e.g., in a complementaryfashion) the two sets of labels. The combined set of labels (e.g., fusedlabels) may be utilized by a supervised machine learning module togenerate models for the application of current sensor data to detectand/or predict faults.

FIG. 6 depicts information fusion in some embodiments. The output (e.g.,labels) of the fault detection model 1 output (e.g., the output of theprimary analytics module 404) may be merged with the output (e.g.,labels) of the fault detection model 2 (e.g., the output of the reduceddimensionality analytics module 406) to create combined labels. Forexample, the merging of the two outputs may be a normalized version ofTsallis entropy

$\left( {{e.g.},{P_{eff} = \left( {\frac{1}{N}{\sum\limits_{i = 1}^{N}P_{{true},i}^{K}}} \right)^{\frac{1}{K}}}} \right).$

The fused output may exploit information obtained from high dimensionaldata and transformation of data possibly correlated variables into a setof values of linearly uncorrelated variables. As a result, thecombination may be a preferred solution where accuracy is improved.

FIG. 7a depicts the results of running an isolation forest analysis onsensor data with a threshold of 50% as an example. In this example, theprimary analytics module 404 may apply isolation forests to extractedfeatures of the historical sensor data. The results are depicted in FIG.7 a.

FIG. 7b depicts the results of running a PCA analysis on sensor datawith a threshold of 50% as an example. In this example, the reduceddimensionality analytics module 406 may apply PCA analysis to the sameextracted features of the same historical sensor data. The results aredepicted in FIG. 7 b.

FIGS. 7a and 7b depict capturing different information from the sameextracted features of the historical sensor data.

FIG. 7c depicts example results of fusing the results of the isolationforest analysis and the PCA analysis. In this example, the fusion module408 may fuse the labels produced from the primary analytics module 404and the reduced dimensionality analytics module 406. The example resultsof FIG. 7c depict an improvement in prediction and accuracy of failuredetection.

Returning to FIG. 4, the model training module 410 may utilize asupervised learning model to create a supervised model capable ofdetecting and/or predicting faults in the assets. In variousembodiments, the model training module 410 utilizes the combined labelsfrom the fusion module 408 to create the supervised model(s). It will beappreciated that many supervised different learning models may beutilized. For example, the model training module 410 may utilize supportvector machines, linear regression, logistic regression, naïve Bayes,linear discriminant analysis, decision trees, K-nearest neighboralgorithm, and neural networks.

The model application module 414 may apply the model(s) generated by themodel training module 410 to new sensor data (e.g., current sensordata). For example, once the model(s) are generated, the componentfailure prediction system 104 may receive current (e.g., new) data fromany number of sensors (e.g., the same sensors that provided thehistorical sensor data and/or other sensors) to detect and/or predictfailures.

The evaluation module 416 may be configured to evaluate the results formthe model application module 414. In various embodiments, the resultsfrom the application of the model(s), the evaluation module 416 mayapply thresholds or triggers to identify failures or predictions offailures (e.g., significant failures or failures of with sufficientconfidence).

The report and alert generation module 418 may generate a reportincluding the results of the application of the model(s) to identifycomponents and/or assets that are expected to suffer a failure (and/orare suffering a failure). In various embodiments, the report mayindicate a timeframe after or at which the failure is expected to occur.The report and alert generation module 418 may provide the report to theoperator, utility, maintenance service devices, and/or the like.

In various embodiments, the report and alert generation module 418 maygenerate an alert based on the results of the application of themodel(s). For example, the report and alert generation module 418 mayprovide alert communications (e.g., email, SMS text, phone calls, and/orthe like) to devices to indicate a significant failure prediction orcurrent failure. The report and alert generation module 418 may comparethe results from the application of the model(s) to any number ofcriteria to determine significance. The criteria may include, but not belimited to, a number of failures in close proximity to each other, anumber of failures, significance of one or more failures (e.g., riskingan asset as a whole, impacting other assets, or impacting the electricalnetwork), and/or the impact the failure may have to critical orimportant services.

The data storage 420 may include any number of data storage devices andor logical storage spaces. The data storage 420 may include, forexample, any number of databases, tables, and/or any other datastructures. The data storage 420 may be configured to store any amountof historical sensor data, current sensor data, extracted features,generated models, labels, results of application of models to currentsensor data, reports, and/or alerts.

In some embodiments, after the model training module 410 generates a newmodel using the fused information, the communication module 402 receivescurrent sensor data, the feature extraction module 412 may extractfeatures from the current sensor data, and the model application module414 may apply the model from the model training module 410 to analyzethe current sensor data to detect and/or predict failures.

FIG. 8 depicts a flow chart for creating models using label fusion insome embodiments. In step 802, the communication module 402 receiveshistorical sensor data. The historical sensor data may include datagenerated by sensors in the past and include known faults of any numberof components of an electrical asset. In step 804, the featureextraction module 412 may extract features from the historical sensordata. The features may include, but not be limited to, dimensions suchas measurements from the sensors or combinations of measurements. Insome embodiments the features may include a function performed on anyamount of sensor data (e.g., any number of measurements).

In step 806, the primary analytics module 404 may analyze extractedfeatures with unsupervised anomaly detection techniques. Such atechnique may include, for example, isolation forests. In step 808, theprimary analytics module 404 may determine first labels. First labelsmay be generated from the unsupervised anomaly detection techniques.

In step 810, the reduced dimensionality analytics module 406 may analyzethe same extracted features with dimensionality reduction techniques.Such a technique may include, for example, PCA. In step 812, the reduceddimensionality analytics module 406 may determine second labels.

In step 814, the fusion module 408 may receive the first labels and thesecond labels to fuse information to create a set of combined labels.The fusion module 408 may combine the first labels and the second labelsin a complementary manner. For example the first labels may be added tothe second labels to generate the combined labels (e.g., reducingduplicates).

In step 816, the model training module 410 may utilize supervisedmachine learning using the combined labels to generate one or moremodels to detect and predict faults. By utilizing a set including all orsome of the first and second labels the model training module 410increases the accuracy of the resulting detection of faults, increasestime for prediction of faults, or both. It will be appreciated that, insome embodiments, by utilizing a set including all or some of the firstand second labels the model training module 410 increases the accuracyof the resulting detection of faults without increasing the time ofprediction.

In step 818, the model training module 410 may provide the one or moremodels to data storage 420 and/or the model application module 414.

FIG. 9 depicts a flow chart for applying models using label fusion insome embodiments. As discussed herein, the component failure predictionsystem 104 may improve and accuracy and/or time to predict failure usingthe combined labels. In step 902, the communication module 402 mayreceive current sensor data. The current sensor data may be from thesame sensors that provided the historical sensor data, differentsensors, or combination of sensors (e.g., those that provided at leastsome of the historical data as well as others).

In step 904, the feature extraction module 412 extract features in thecurrent sensor data (e.g., the recently received sensor data as opposedto historical data received in the past). In some embodiments, thefeature extraction module 412 extract features from the current sensordata in the same manner as features that were extracted from thehistorical sensor data.

In step 906, the model application module 414 applies the models fromthe model training module 410 to the extracted features of the currentsensor data to detect and/or predict failures. As discussed herein, themodel from the model training module 410 may be generated using thecombined labels from the fusion module 408.

In step 908, report and alert generation module 418 generates reportsidentifying predictive failures and identifying assets or components ofassets. In some embodiments the report may identify the significance ofthe type of failure, the impact of the failure of the asset, the impactof failure on the network, the expected timeframe of the failure, theimpact of failure on critical services (such as on a hospital or duringextreme weather), and/or the like.

FIG. 10 depicts an example report for fault and prediction analysis foran electrical asset using information fusion. In this example, knownfaults were accurately predicted 45 days in advance of the fault, usingdifferent threshold predictions. For example, using a 30% threshold, 40%threshold, or 50% threshold, faults are accurately predicted 45 days inadvance.

FIG. 11 depicts an example report for fault and prediction analysis foranother electrical asset using information fusion. In this example,known faults were accurately predicted 45 days in advance of the fault,using different threshold predictions. For example, using a 30%threshold, 40% threshold, or 50% threshold, faults are accuratelypredicted 45 days in advance.

FIG. 12 depicts an example report for fault and prediction analysis fora third electrical asset using information fusion. In this example,there are no faults in the historical data, and no faults were predictedusing the fault and prediction analysis based on information fusion asdiscussed herein.

FIG. 13 depicts a block diagram of an example computer system 1300according to some embodiments. Computer system 1300 is shown in the formof a general-purpose computing device. Computer system 1300 includesprocessor 1302, RAM 1304, communication interface 1306, input/outputdevice 1308, storage 1310, and a system bus 1312 that couples varioussystem components including storage 1310 to processor 1302.

System bus 1312 represents one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnect (PCI) bus.

Computer system 1300 typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby the computer system 1300 and it includes both volatile andnonvolatile media, removable and non-removable media.

In some embodiments, processor 1302 is configured to execute executableinstructions (e.g., programs). In some embodiments, the processor 1004comprises circuitry or any processor capable of processing theexecutable instructions.

In some embodiments, RAM 1304 stores data. In various embodiments,working data is stored within RAM 1304. The data within RAM 1304 may becleared or ultimately transferred to storage 1310.

In some embodiments, communication interface 1306 is coupled to anetwork via communication interface 1306. Such communication can occurvia Input/Output (I/O) device 1308. Still yet, the computer system 1300may communicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet).

In some embodiments, input/output device 1308 is any device that inputsdata (e.g., mouse, keyboard, stylus) or outputs data (e.g., speaker,display, virtual reality headset).

In some embodiments, storage 1310 can include computer system readablemedia in the form of volatile memory, such as read only memory (ROM)and/or cache memory. Storage 1310 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage 1310 can be provided for readingfrom and writing to a non-removable, non-volatile magnetic media (notshown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CDROM, DVD-ROM or other optical media can be provided. Insuch instances, each can be connected to system bus 1312 by one or moredata media interfaces. As will be further depicted and described below,storage 1310 may include at least one program product having a set(e.g., at least one) of program modules that are configured to carry outthe functions of embodiments of the invention. In some embodiments, RAM1304 is found within storage 1310.

Program/utility, having a set (at least one) of program modules, such asthose contained within the component failure prediction system 104, maybe stored in storage 1310 by way of example, and not limitation, as wellas an operating system, one or more application programs, other programmodules, and program data. Each of the operating system, one or moreapplication programs, other program modules, and program data or somecombination thereof, may include an implementation of a networkingenvironment. Program modules generally carry out the functions and/ormethodologies of embodiments of the invention as described herein. Amodule may be hardware (e.g., ASIC, circuitry, and/or the like),software, or a combination of both.

It should be understood that although not shown, other hardware and/orsoftware components could be used in conjunction with the computersystem 1300. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, and external disk drivearrays, RAID systems, tape drives, and data archival storage systems,etc.

Exemplary embodiments are described herein in detail with reference tothe accompanying drawings. However, the present disclosure can beimplemented in various manners, and thus should not be construed to belimited to the embodiments disclosed herein. On the contrary, thoseembodiments are provided for the thorough and complete understanding ofthe present disclosure, and completely conveying the scope of thepresent disclosure to those skilled in the art.

As will be appreciated by one skilled in the art, aspects of one or moreembodiments may be embodied as a system, method or computer programproduct. Accordingly, aspects may take the form of an entirely hardwareembodiment, an entirely software embodiment (including firmware,resident software, micro-code, etc.) or an embodiment combining softwareand hardware aspects that may all generally be referred to herein as a“circuit,” “module” or “system.” Furthermore, aspects may take the formof a computer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The invention claimed is:
 1. A non-transitory computer readable mediumcomprising executable instructions, the executable instructions beingexecutable by one or more processors to perform a method, the methodcomprising: receiving historical sensor data of a first time period, thehistorical sensor data including sensor data from one or more sensors ofa renewable energy asset; extracting features from the historical sensordata; performing a unsupervised anomaly detection technique on thehistorical sensor data to generate first labels associated with thehistorical sensor data; performing at least one dimensionality reductiontechnique to generate second labels associated with the historicalsensor data, the dimensionality reduction technique reducing a secondnumber of dimensions of the extracted features when compared to a firstnumber of dimensions of the extracted features analyzed using theunsupervised anomaly detection; combining the first labels and thesecond labels to generate combined labels; generating one or more modelsbased on supervised machine learning and the combined labels; receivingcurrent sensor data of a second time period, the current sensor dataincluding sensor data from at least a subset of the one or more sensorsof the renewable energy asset; extracting features from the currentsensor data; applying the one or more models to the extracted featuresof the current sensor data to create a prediction of a future fault inthe renewable energy asset; and generating a report including theprediction of the future fault in the renewable energy asset.
 2. Thenon-transitory computer readable medium of claim 1, wherein theunsupervised anomaly detection technique includes an isolation foresttechnique.
 3. The non-transitory computer readable medium of claim 1,wherein the at least one dimensionality reduction technique includes aprincipal component analysis (PCA) technique.
 4. The non-transitorycomputer readable medium of claim 1, wherein combining the first andsecond labels are combined in a complimentary manner.
 5. Thenon-transitory computer readable medium of claim 1, wherein generatingone or more models based on supervised machine learning and the combinedlabels includes K-nearest neighbor algorithm or a neural network.
 6. Thenon-transitory computer readable medium of claim 1, wherein supervisedmachine learning includes k-means clustering.
 7. The non-transitorycomputer readable medium of claim 1, wherein performing the unsupervisedanomaly detection technique on the historical sensor data to generatethe first labels comprises generating an anomaly score based on anoutput of the unsupervised anomaly detection technique and comparing theanomaly score to a threshold to determine if at least one of the firstlabels should be generated.
 8. The non-transitory computer readablemedium of claim 1, wherein performing the at least one dimensionalityreduction technique to generate the second labels comprises generating az-score based on an output of the at least one dimensionality reductiontechnique and comparing the z-score to a threshold to determine if atleast one of the second labels should be generated.
 9. Thenon-transitory computer readable medium of claim 1, the method furthercomprising comparing a fault prediction against a criteria to determinesignificance of one or more predicted faults and generating an alertbased on the comparison, the alert including generating a messageidentifying the one or more predicted faults.
 10. A system, comprisingat least one processor; and memory containing instructions, theinstructions being executable by the at least one processor to: receivehistorical sensor data of a first time period, the historical sensordata including sensor data from one or more sensors of a renewableenergy asset; extract features from the historical sensor data; performa unsupervised anomaly detection technique on the historical sensor datato generate first labels associated with the historical sensor data;perform at least one dimensionality reduction technique to generatesecond labels associated with the historical sensor data, thedimensionality reduction technique reducing a second number ofdimensions of the extracted features when compared to a first number ofdimensions of the extracted features analyzed using the unsupervisedanomaly detection; combine the first labels and the second labels togenerate combined labels; generate one or more models based onsupervised machine learning and the combined labels; receive currentsensor data of a second time period, the current sensor data includingsensor data from at least a subset of the one or more sensors of therenewable energy asset; extract features from the current sensor data;apply the one or more models to the extracted features of the currentsensor data to create a prediction of a future fault in the renewableenergy asset; and generate a report including the prediction of thefuture fault in the renewable energy asset.
 11. The system of claim 10,wherein the unsupervised anomaly detection technique includes anisolation forest technique.
 12. The system of claim 10, wherein the atleast one dimensionality reduction technique includes a principalcomponent analysis (PCA) technique.
 13. The system of claim 10, whereincombining the first and second labels are combined in a complimentarymanner.
 14. The system of claim 10, wherein generating one or moremodels based on supervised machine learning and the combined labelsincludes K-nearest neighbor algorithm or a neural network.
 15. Thesystem of claim 10, wherein supervised machine learning includes k-meansclustering.
 16. The system of claim 10, wherein performing theunsupervised anomaly detection technique on the historical sensor datato generate the first labels comprises generating an anomaly score basedon an output of the unsupervised anomaly detection technique andcomparing the anomaly score to a threshold to determine if at least oneof the first labels should be generated.
 17. The system of claim 10,wherein performing the at least one dimensionality reduction techniqueto generate the second labels comprises generating a z-score based on anoutput of the at least one dimensionality reduction technique andcomparing the z-score to a threshold to determine if at least one of thesecond labels should be generated.
 18. The system of claim 10, theinstructions being further executable by the at least one processor tocompare a fault prediction against a criteria to determine significanceof one or more predicted faults and generating an alert based on thecomparison, the alert including generating a message identifying the oneor more predicted faults.
 19. A method comprising: receiving historicalsensor data of a first time period, the historical sensor data includingsensor data from one or more sensors of a renewable energy asset;extracting features from the historical sensor data; performing aunsupervised anomaly detection technique on the historical sensor datato generate first labels associated with the historical sensor data;performing at least one dimensionality reduction technique to generatesecond labels associated with the historical sensor data, thedimensionality reduction technique reducing a second number ofdimensions of the extracted features when compared to a first number ofdimensions of the extracted features analyzed using the unsupervisedanomaly detection; combining the first labels and the second labels togenerate combined labels; generating one or more models based onsupervised machine learning and the combined labels; receiving currentsensor data of a second time period, the current sensor data includingsensor data from at least a subset of the one or more sensors of therenewable energy asset; extracting features from the current sensordata; applying the one or more models to the extracted features of thecurrent sensor data to create a prediction of a future fault in therenewable energy asset; and generating a report including the predictionof the future fault in the renewable energy asset.